The present invention is generally related to statistics, marketing, and experimental design and more particularly related to an expert system that uses split-run and factorial design methods to determine which factors are most important in an experiment.
Marketing is a process through which a company induces new and existing customers to buy its products and services. One familiar type of a marketing activity is advertising, where a company broadcasts its message to whomever is viewing the medium carrying the advertising message, for example newspapers, television, billboards, web sites, even the sides of buses. Another type of marketing activity is direct marketing, in which a company tries to address its prospects and customers individually through postal mail or email.
Targeting is the process of selecting potential buyers, perhaps for particular products or their likelihood of making a purchase in the near future or because they may be in danger of defecting, among other reasons. Properly done, targeting should also include predicting the results of the actual campaign. Testing is the process of experimenting to determine the most effective offers or the right customers to target. Campaigning involves contacting the targeted customers by appropriate media, such as email or direct mail.
Like other researchers, marketers need to carefully design experiments to effectively test marketing campaigns. For example, they may need to determine whether an optimum discount is, for example, 5% or 10% or a $10 off coupon. They typically need to determine what level of personalization is most effective, and what communications channels work best. Each of these variables are called Factors. To get the answers to questions like this, Marketers design small campaigns to test what value of each factor works best.
Historically the process has been to use a main population and a control group to measure the effect of a factor. The main population gets a campaign with value1 for factor1. Factor1 might be discount coupon rate. Value1 might be $10 off on $50 of purchases. The control group consists of a population with the same characteristics as the main group, but with a different value, value2, for the factor, for example $20 off on $100 of purchases. This simple kind of design is called A/B, Split-run, or more commonly One Factor At a Time (OFAT) design. Only one factor is changed. When OFAT design is used, several campaigns are needed to test multiple factors.
Advances in statistical analysis have led to a much improved methodology called factorial design in which several factors can be tested in the same campaign. Adoption of factorial design has been slow because tests can be difficult to design and hard to interpret, especially when the number of factors grows or partial factorial designs are used. However factorial design experiments have several advantages:                Results are obtained sooner because multiple campaigns are not needed        Costs are lower because smaller subject populations are used and fewer campaigns are launched        Interactions between factors can be measured, which is close to impossible with split-run designs        